class_docstr: Convolution-augmented Emformer layer that constitutes ConvEmformer.
  
  Args:
      input_dim (int): input dimension.
      num_heads (int): number of attention heads.
      ffn_dim: (int): hidden layer dimension of feedforward network.
      segment_length (int): length of each input segment.
      kernel_size (int): size of kernel to use in convolution module.
      dropout (float, optional): dropout probability. (Default: 0.0)
      ffn_activation (str, optional): activation function to use in feedforward network.
          Must be one of ("relu", "gelu", "silu"). (Default: "relu")
      left_context_length (int, optional): length of left context. (Default: 0)
      right_context_length (int, optional): length of right context. (Default: 0)
      max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
      weight_init_gain (float or None, optional): scale factor to apply when initializing
          attention module parameters. (Default: ``None``)
      tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
      negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
      conv_activation (str, optional): activation function to use in convolution module.
          Must be one of ("relu", "gelu", "silu"). (Default: "silu")
code: def _pack_state(
      self,
      next_k: torch.Tensor,
      next_v: torch.Tensor,
      update_length: int,
      mems: torch.Tensor,
      conv_cache: torch.Tensor,
      state: List[torch.Tensor],
  ) -> List[torch.Tensor]:
      new_k = torch.cat([state[1], next_k])
      new_v = torch.cat([state[2], next_v])
      state[0] = torch.cat([state[0], mems])[-self.max_memory_size :]
      state[1] = new_k[new_k.shape[0] - self.left_context_length :]
      state[2] = new_v[new_v.shape[0] - self.left_context_length :]
      state[3] = state[3] + update_length
      state[4] = conv_cache
      return state
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 4 for calling the _ConvEmformerLayer obj's underlying member method code with example usage: obj._pack_state(next_k, next_v, update_length, mems, conv_cache, state) and returns: state